The article further delves into the binary world of computing, explaining how numbers are stored inside a computer in a floating-point format, usually using 16 or 32 bits. The goal is to reduce this to 1-bit, which would dramatically decrease the memory or storage needed and speed up processing. However, this approach can result in a loss of crucial information. The article also discusses the possibility of using a ternary value system of -1, 0, +1, which would require 2-bits rather than 1-bit, but could potentially preserve more information. The article concludes by noting that the use of 1-bit or low-bit models could benefit both small and large language models, potentially aiding the goal of achieving artificial general intelligence (AGI).
Key takeaways:
- The article discusses the potential of 1-bit large language models (LLMs) in the realm of generative AI, which can function well in low-resource situations, such as running a full-scale AI app on a smartphone without internet connection.
- 1-bit LLMs or small language models (SLMs) are a form of technological advancement that can compress or compact full-size LLMs into a smaller overall package, making them less resource-intensive while maintaining desired capabilities.
- 1-bit LLMs work by converting floating-point values into binary values of 0 and 1, which significantly reduces the memory or storage needed and speeds up processing. However, this method can lose some information due to rounding.
- There are debates about the best way to implement 1-bit LLMs, including whether to use quantization-aware training (QAT) or post-training quantization (PTQ), and whether to represent binary values as 0 and 1 or -1 and +1. Some researchers are also exploring the use of ternary value systems of -1, 0, +1, which could average out to use approximately 1.5 bits.